<html>
   <head>
      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
   
      <link rel="stylesheet" href="./../../helpwin.css">
      <title>MATLAB File Help: prtKernelPolynomial/prtKernelPolynomial</title>
   </head>
   <body>
      <!--Single-page help-->
      <table border="0" cellspacing="0" width="100%">
         <tr class="subheader">
            <td class="headertitle">MATLAB File Help: prtKernelPolynomial/prtKernelPolynomial</td>
            
            
         </tr>
      </table>
      <div class="title">prtKernelPolynomial/prtKernelPolynomial</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtKernelPolynomial</span>  Polynomial kernel object
 
   kernelObj = <span class="helptopic">prtKernelPolynomial</span>; Generates a kernel object implementing a
   polynomial kernel.  Kernel objects are widely used in several
   prt classifiers, such as prtClassRvm and prtClassSvm.  Polynomial kernels
   implement the following function for 1 x N vectors x1 and x2:
 
    k(x,y) = (x*y'+c).^d;
 
   KERNOBJ = <span class="helptopic">prtKernelPolynomial</span>(PROPERTY1, VALUE1, ...) constructs a
   <span class="helptopic">prtKernelPolynomial</span> object KERNOBJ with properties as specified by
   PROPERTY/VALUE pairs. <span class="helptopic">prtKernelPolynomial</span> objects have the following
   user-settable properties:
 
    d   - Positive scalar value specifying the order of the polynomial.
          (Default value is 2)
 
    c   - Positive scalar indicating the offset of the polynomial.
          (Default value is 0)
 
    <span class="helptopic">prtKernelPolynomial</span> objects inherit the TRAIN, RUN, and AND
    methods from prtKernel.
 
   Polynomial kernels are widely used in the machine
   learning literature. For more information on these kernels, please
   refer to:
    
   <a href="http://en.wikipedia.org/wiki/Support_vector_machine#Non-linear_classification">http://en.wikipedia.org/wiki/Support_vector_machine#Non-linear_classification</a>
 
   % Example:
    ds = prtDataGenBimodal;         % Load a data set
    k1 = <span class="helptopic">prtKernelPolynomial</span>;       % Create 2 kernels to compare
    k2 = <span class="helptopic">prtKernelPolynomial</span>('d',3);
    
    k1 = k1.train(ds); % Train
    g1 = k1.run(ds);   % Evaluate
 
    k2 = k2.train(ds); % Train
    g2 = k2.run(ds);   % Evaluate
 
    subplot(2,1,1); imagesc(g1.getObservations);  %Plot the results
    subplot(2,1,2); imagesc(g2.getObservations);</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./../prtKernel.html">prtKernel</a>,<a href="./../prtKernelSet.html">prtKernelSet</a>, <a href="./../prtKernelDc.html">prtKernelDc</a>, <a href="./../prtKernelDirect.html">prtKernelDirect</a>,
    <a href="./../prtKernelHyperbolicTangent.html">prtKernelHyperbolicTangent</a>, <a href="./../prtKernelRbf.html">prtKernelRbf</a>,
    <a href="./../prtKernelRbfNdimensionScale.html">prtKernelRbfNdimensionScale</a>, 
</div>
   </body>
</html>